Storage Life Prediction Method of the Aerospace Electromagnetic Relays Based on Physics of Failure and Data-Driven Fusion

The storage life prediction of the aerospace electromagnetic relays(AEMRs) has become an engineering challenge due to the nonlinearity of AEMRs’ complex degradation process. As the widely used life prediction approaches, data-driven methods also failed to address this issue effectively. The main reason is their inherent flaw: the inability to effectively quantify the direct correlation between top-level failures and underlying physical or chemical changes. Therefore, based on the fusion of the physics of failure(PoF) and data-driven, a storage life prediction method for AEMR has been proposed in this manuscript. Firstly, according to the characteristics of the AEMR during storage, the degradation performance parameters were analyzed, and the accurate expression of the degradation model was established based on the failed physical process. Then, the degradation process of AEMR was modelled by fusion of the PoF and data-driven, while the model parameters were updated by an improved particle filter based on degradation data. Finally, the storage life of AEMR was predicted by the updated model. Compared with typical methods and machine-learning-based methods, on the one hand, the results show that the proposed method can restrain the fluctuation of nonlinear degradation data to some extent and make the fluctuation range of prediction error more stable, thus making the life prediction result more accurate. On the other hand, even when there is only a small sample size of observation data, it also gives a good prediction effect. Moreover, it establishes the relationship between life and underlying physical and chemical processes, which will be conducive to the optimization design of AEMR, to further improve the reliability of weapons and equipment.